Build Applications with Local AI Models on a Mac (MEAP 06)
Build Applications with Local AI Models on a Mac (MEAP 06) | 8.89 MB
Title: Build Applications with Local AI Models on a Mac (MEAP 06)
Author: Keiji Kamigusa
Category: Nonfiction, Computers, Advanced Computing, Natural Language Processing, Artificial Intelligence, Networking & Communications
Language: English | 351 Pages | ISBN: 6610001304201
Description:
If you have ever stared at an API pricing page wondering whether you should pay $2 per million tokens or $50 per million tokens, this book is for you. If you have ever received a cloud bill that was 3 times higher than expected because an AI agent ran in a loop for 6 hours, this book is definitely for you. And if you have ever tried to explain to a CFO why the company spent $12,000 on language model API calls last month, this book might just save your career. The dirty secret of the current AI boom is that most teams have no real idea what their AI infrastructure actually costs them. The gap between expected cost and actual cost is routinely an order of magnitude.
The landscape of available AI models has exploded from a handful of options to hundreds of distinct models across dozens of providers. Every week brings a new release, a new price cut, or a new benchmark that reshuffles the rankings. For developers, product managers, and technical leaders, the task of choosing the right model for the right job has transformed from a simple yes-or-no decision into a complex optimization problem with multiple variables. The cost per token, once a dry technical specification, has become one of the most important metrics in modern software development.
Inside, you'll discover:
• Understanding token math, how providers price input, output, and reasoning tokens
• The great provider landscape and how to compare OpenAI, Anthropic, Google, DeepSeek, and others
• Reading between the price lines, batch discounts, caching economics, and hidden fees
• The prompt caching advantage and how to achieve 50-90% discounts on repeated tokens
• Model routing as a cost strategy, sending simple tasks to cheap models
• Prompt engineering for the bottom line and reducing token consumption by 30-50%
• Building your cost budget, monitoring spend, and deciding when to self-host
The guidance in these pages draws from real production deployments handling millions of requests per day. Whether you are a solo developer building your first AI-powered feature or a platform engineer designing a multi-model infrastructure for thousands of concurrent users, the principles here will help you build systems that are both powerful and economical.
DOWNLOAD:
https://rapidgator.net/file/9058a6baba58cfbdc3a84800989d9531/Build_Applications_with_Local_AI_Models_on_a_Mac.rar
https://nitroflare.com/view/4CDF50FB14A84CB/Build_Applications_with_Local_AI_Models_on_a_Mac.rar
If you have ever stared at an API pricing page wondering whether you should pay $2 per million tokens or $50 per million tokens, this book is for you. If you have ever received a cloud bill that was 3 times higher than expected because an AI agent ran in a loop for 6 hours, this book is definitely for you. And if you have ever tried to explain to a CFO why the company spent $12,000 on language model API calls last month, this book might just save your career. The dirty secret of the current AI boom is that most teams have no real idea what their AI infrastructure actually costs them. The gap between expected cost and actual cost is routinely an order of magnitude.
The landscape of available AI models has exploded from a handful of options to hundreds of distinct models across dozens of providers. Every week brings a new release, a new price cut, or a new benchmark that reshuffles the rankings. For developers, product managers, and technical leaders, the task of choosing the right model for the right job has transformed from a simple yes-or-no decision into a complex optimization problem with multiple variables. The cost per token, once a dry technical specification, has become one of the most important metrics in modern software development.
Inside, you'll discover:
• Understanding token math, how providers price input, output, and reasoning tokens
• The great provider landscape and how to compare OpenAI, Anthropic, Google, DeepSeek, and others
• Reading between the price lines, batch discounts, caching economics, and hidden fees
• The prompt caching advantage and how to achieve 50-90% discounts on repeated tokens
• Model routing as a cost strategy, sending simple tasks to cheap models
• Prompt engineering for the bottom line and reducing token consumption by 30-50%
• Building your cost budget, monitoring spend, and deciding when to self-host
The guidance in these pages draws from real production deployments handling millions of requests per day. Whether you are a solo developer building your first AI-powered feature or a platform engineer designing a multi-model infrastructure for thousands of concurrent users, the principles here will help you build systems that are both powerful and economical.
DOWNLOAD:
https://rapidgator.net/file/9058a6baba58cfbdc3a84800989d9531/Build_Applications_with_Local_AI_Models_on_a_Mac.rar
https://nitroflare.com/view/4CDF50FB14A84CB/Build_Applications_with_Local_AI_Models_on_a_Mac.rar
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